Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| HappyHorse 1.0 T2V alibaba/hh1-t2v | Sonar Deep Research perplexity/sonar-deep-research | |
|---|---|---|
| Provider | Alibaba | Perplexity |
| Family | HappyHorse | |
| Modality | video | text |
| Context window | — | 127,000 tok |
| Max output | — | 16,384 tok |
| Released | 2026-04-27 | 2025-02-14 |
| Input price | $0.280 /sec | $2.00 /1M |
| Output price | — | $8.00 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | yes |
| Prompt caching | — | — |
Alibaba's HappyHorse 1.0 text-to-video model. Generates videos from a text prompt with configurable resolution, aspect ratio, and duration (3-15s).
Perplexity Sonar Deep Research — long-form web-grounded answers.
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try HappyHorse 1.0 T2V
client.chat.completions.create(
model="alibaba/hh1-t2v",
messages=[{"role":"user","content":"hello"}],
)
# Try Sonar Deep Research — same client, same key
client.chat.completions.create(
model="perplexity/sonar-deep-research",
messages=[{"role":"user","content":"hello"}],
)